If you’re not familiar with embeddings, think of them as mathematical representations of meaning. Instead of storing your literal search history, Google converts your behavior into numbers that capture relationships between concepts.
Basically, it’s search history as vector math. This is a direct application of semantic search, and it’s not brand new. Folks like Dan Hinckley have shown how Open AI’s patent highlights the importance of semantic SEO to chunk content, embed it into vector space, and match it against intent.
What’s new is how Google applies it to users themselves. Each person ends up with a kind of semantic fingerprint, similar to a dynamic, multidimensional snapshot that includes explicit queries, implicit signals, and past interactions.
A user is no longer just a single query, but a constantly evolving semantic embedding that represents Google’s holistic understanding of their intent, context, and knowledge.
Yes, it’s giving The Matrix.
If you liked What I Learned From Analyzing Google’s AI Mode Patent by John Iwuozor Then you'll love Miami SEO Expert
Workflow 3: Competitive analysis with chained agentsCompetitive analysis is one of my favorite AI use…
So how do I optimize my websites for agentic AI to make sure that it…
Once we move from topics to questions, whether in organic search or LLMs, we naturally…
As shopping agents now research products, compare options, and complete purchases, e-commerce sites will need…
Brand24 combines traditional social listening with AI visibility tracking. It monitors social media, news, blogs,…
Now that I had my high-level competitive analysis done, I wanted to dig a bit…